131 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 131 plugins that match these filters.
magnus-agent-skillsmagnus919/agent-skills3
benai-skillsnaveedharri/benai-skills1
arsenalmirkobozzetto/arsenal1
a-tokyo-skillsa-tokyo/agent-skills1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceDesign and improve human annotation programs for machine-learning training, validation, and evaluation data, including task and label schemas, active acquisition, annotator guidance, calibration, disagreement, adjudication, provenance, and cost-quality stopping. Do not use for general statistical inference, data storage pipelines, model training, or annotation-interface implementation; route those concerns to data-scientist, data-engineering, ml-engineering, or product-design-and-ux.
Verify work against explicit criteria using direct, source-faithful evidence, reproducible checks, and clear verdicts. Use before declaring an artifact, implementation, or claim complete; do not use for exploratory research without pass/fail criteria.
Operate Vite projects: inspect versions and configuration, run bounded development and production builds, diagnose dependency and asset failures, and configure environment-aware frontend delivery. Use when a task names Vite, vite.config, Vite plugins, import.meta.env, dev-server behavior, or Vite build output. Do not use for frontend architecture or visual implementation (route to frontend-engineering), CI/platform provisioning (route to platform-engineering), browser automation (route to playwright), or accessibility design (route to web-accessibility).
Build, test, and deploy n8n workflows through incremental REST API automation. Convert discovery call transcripts into implementation-ready automation blueprints.
Focused implementation workflow plus optional continuous Arsenal routing for OMP and Pi.
implementationDoer to verifier-panel to consensus delivery verification for any artifact (code slices, plans, documents, audits). An orchestrator freezes acceptance criteria before implementation, dispatches a doer, then convenes a context-walled panel of independent verifiers — including an adversary with a must-oppose mandate — for evidence-anchored review adjudicated to SHIP / SHIP_WITH_CAVEATS / ITERATE / BLOCK / ESCALATE. Platform-agnostic; degrades to sequential fresh-context sessions without subagents.
Ten skills forming a documentation chain: AGENTS.md entry point, PRD, technical specs, ADRs, implementation plans, status tracking with crash recovery, a git workflow with evidence-carrying PRs, an independent verification gate, gated documentation maintenance, and a brownfield onboarding on-ramp — plus a bundled verifier agent and router + run-loop drivers shipped as both commands and skills (works in Claude Code, GitHub Copilot CLI, and Codex). The harness layer for long-running, loop-driven development.
workflowsThree-agent workflow for orchestrated software delivery with an orchestrator plus implementation and QA subagents.
Repository analysis, technical design, implementation, debugging, tests, and Git delivery. Apply these capabilities directly to the requested engineering outcome.
Adaptive implementation-grounded learning for Claude Code.
Shift-left QA: testability reviews of specs before implementation, non-functional requirements turned into testable thresholds, and STRIDE threat modeling. Acceptance-criteria extraction and Gherkin coverage mapping live in qa-bdd; data-contract extraction lives in qa-data-quality.
operations-resilienceStrategic thinking, planning, and implementation — forge, apex, ultrapex, oneshot
Build high-performance Model Context Protocol servers in Rust using the official rmcp SDK with async/await, procedural macros, and type-safe implementations.
Tools for creation, management and research of technical spikes to reduce unknowns and assumptions before proceeding to specification and implementation of solutions.
Guided implementation of ZenML MLOps best practices including metadata logging, experiment tracking, alerts, schedules and triggers, secrets, streaming events, and Model Control Plane setup
Premium planning, thrifty implementation
Creates detailed, sectionized implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.
Clarify ambiguous requirements through focused dialogue before implementation. Use when requirements are unclear or features are complex.
Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework.
Visualize UI concepts through ASCII mockups before implementation. Creates multiple ASCII mockup variations for dashboard layouts, forms, and other UI components.
